Open Access
31 October 2018 Using the modified two-mode method to identify surface water in Gaofen-1 images
Zhiyuan Zhang, Haixia He, Changhui Yu, Wen Zhang, Linyi Li, Lingkui Meng
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Abstract
The rapid, accurate, and automated extraction of surface water is highly important for conducting reliable and necessary surface water monitoring endeavors. Classification methods commonly exhibit high precision but also have a low degree of automation or narrow scope of application; commonly used water index methods are highly efficient, but they easily mistake other targets with similar spectral characteristics for surface water. Simultaneously achieving precision, efficiency, and automation within a single method is a challenge. To address these problems, we simplify the normalized different water index (NDWI) to a band ratio index and traverse the neighborhood of the extreme in the histogram to determine two peaks and one trough between the peaks in the two-mode method, and we then compare the middle value of the two peaks with the value of the trough to confirm the threshold of the surface water. We use the modified two-mode method to extract Poyang Lake from four Chinese Gaofen (GF)-1 remote sensing images corresponding to different seasons, and then compare the results with those obtained by the NDWI index and the maximization of interclass variance (OTSU) method. The comparison shows that our method has higher and more stable accuracy, especially during the drought period for Poyang Lake. However, polluted water, narrow rivers, bridges, and residential areas along the lake are sometimes mistakenly extracted. Finally, the advantages and prospects of the proposed method are discussed.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Zhiyuan Zhang, Haixia He, Changhui Yu, Wen Zhang, Linyi Li, and Lingkui Meng "Using the modified two-mode method to identify surface water in Gaofen-1 images," Journal of Applied Remote Sensing 13(2), 022003 (31 October 2018). https://doi.org/10.1117/1.JRS.13.022003
Received: 22 May 2018; Accepted: 11 September 2018; Published: 31 October 2018
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CITATIONS
Cited by 9 scholarly publications.
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KEYWORDS
Image segmentation

Remote sensing

Water

Image classification

Near infrared

Satellites

Reflectivity

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